Identifikasi Lahan Investasi Potensial Kawasan Free Trade Zone (FTZ) Bintan
Bibliographic record
Abstract
Free Trade Zone (FTZ) merupakan kawasan yang terpisah dari daerah pabean sehingga bebas dari pengenaan bea masuk, PPN, PPnBM, dan cukai. Salah satu Kawasan FTZ di Indonesia adalah FTZ Bintan. Berada di sisi jalur perdagangan internasional paling ramai di dunia menjadikan Bintan sebagai pintu gerbang arus masuk investasi, barang dan jasa dari dan ke luar negeri. Ketersediaan SDM dan lahan serta komitmen Pemda sangat mendukung bertumbuhnya investasi. Maka dibutuhkan suatu instrumen bagi calon investor terkait ketersediaan, sebaran, dan kondisi lahan-lahan potensial untuk investasi. Dengan pendekatan deskriptif-kualitatif dan menggunakan teknik analisis spasial serta keakuratan data citra satelit yang diperoleh, dapat dihasilkan suatu instrumen akademis dan komprehensif. Data citra satelit dilakukan koreksi orthorektifikasi dan georeferencing untuk menguji validitasnya dan selanjutnya di digitasi, atribusi, dan dilakukan penampalan terhadap peraturan tata ruang serta analisis terhadap kriteria kondisi lahan yang mendukung investasi untuk menghasilkan lahan-lahan potensial untuk investasi. Hasil analisis menunjukkan 1.679,53Ha (4,23%) dan 15.423,92Ha (38,80%) dari total Kawasan FTZ Bintan termasuk kategori lahan potensial sangat tinggi dan tinggi. Lahan-lahan tersebut didominasi semak belukar, tegalan dan lahan-lahan terbuka, memiliki aksesibilitas jalan yang baik serta aksesibilitas yang tinggi terhadap simpul-simpul transportasi sebagai askes keluar masuk barang, jasa dan tenaga kerja.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".